The goal of this proposal is to develop a new computational method to efficiently quantify protein-ligand association in a way that explicitly considers protein flexibility. Molecular recognition between molecules through noncovalent association plays a fundamental role in virtually all processes in biological systems. Although many computational concepts exist to simulate drug-protein recognition, an efficient and accurate quantification of these interactions has still not been achieved. We propose a novel computational method that addresses some of the most serious shortcomings of present approaches: protein flexibility and a reliable quantification of binding affinities. We introduce the new concept of a hypothetical 'ligand model': a virtual ligand that binds to the protein and dynamically changes its shape and properties during molecular dynamics (MD) simulations, essentially representing a large ensemble of different chemical species binding to the same target protein. This approach allows sampling protein conformations relevant to its interaction with chemicals or drug candidates. This method also will allow us to probe conformational flexibility of the protein upon ligand binding. The 'ligand-model' concept will result in an efficient decoupling of sampling using MD simulations and subsequent docking. This method consequently combines both accuracy in quantifying molecular recognition and efficiency in virtual screening of large compound libraries. The software is anticipated to be of wide interest for researchers in all areas of protein-ligand interactions, including drug design, structural biology, and environmental toxicology. [unreadable] [unreadable] PUBLIC HEALTH RELEVANCE Molecular recognition between molecules through noncovalent association plays a fundamental role in virtually all processes in biological systems. This project is aimed toward developing a novel computational method to efficiently quantify protein-ligand binding, explicitly including the dynamics of the protein. It will have wide applicability for drug design and environmental toxicology. [unreadable] [unreadable] [unreadable]